Nonparametric Bayesian Density Estimation with Gaussian Processes
Date
2024
Authors
Advisors
Journal Title
Journal ISSN
Volume Title
Repository Usage Stats
views
downloads
Abstract
This thesis presents a comprehensive study on nonparametric Bayesian density estimation using Gaussian processes (GP). We explore the logistic Gaussian Process (LGP) and introduce an innovative approach termed the tree-logistic-link Gaussian process (TLLGP). This method aims to improve computational efficiency while maintaining modeling flexibility. We address the computational challenges traditionally associated with LGP by implementing a novel tree-based strategy, thereby reducing the complexity of posterior computations. Through a series of numerical experiments, we demonstrate the effectiveness of TLLGP in various scenarios, comparing its performance with other methods. The results highlight the advantages of our approach in terms of computational speed and accuracy in density estimation tasks. This work contributes to the fields of Bayesian statistics and machine learning by providing a more efficient tool for density estimation, especially beneficial for large high-dimensional data where traditional methods fall short due to their computational demands.
Type
Department
Description
Provenance
Subjects
Citation
Permalink
Citation
Wang, Haoxuan (2024). Nonparametric Bayesian Density Estimation with Gaussian Processes. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/31090.
Collections
Except where otherwise noted, student scholarship that was shared on DukeSpace after 2009 is made available to the public under a Creative Commons Attribution / Non-commercial / No derivatives (CC-BY-NC-ND) license. All rights in student work shared on DukeSpace before 2009 remain with the author and/or their designee, whose permission may be required for reuse.